1. Field of the Invention
The present invention relates in general to processing digital data, and in particular, to a system and method for performing corner guided curve matching of multiple images representing a scene for stereo vision applications.
2. Related Art
For stereo vision applications, accurate vector or mathematical representations of objects or scenes derived from two dimensional image data are very important. Vector or mathematical representations of a scene can be comprised of, for example, comer and curve information of an image. Typically, corners and curves are important image features in many vision-based applications. Corners are usually more stable and easier to match than curves, while curves contain richer information of scene structure. In prior systems, corners were often used to recover the epipolar geometry between two views, which was then used in curve matching to reduce the search space. However, information of the scene structure contained in this set of matched corners was typically ignored.
Corners are 2D image points where the image intensity surface has the highest curvatures. Detected corners are usually stable. That is, corners across views corresponding to the same physical point in the 3D world can usually be consistently detected despite different perspective distortions. Current corner matching techniques such as robust estimation and gradient disparity limit allow high rates of correct matching for many real applications.
However, although, corner features are usually used to compute the camera motion because of reliability of their matching, they are not suitable for the scene reconstruction. First, the set of corners is usually very sparse, and only provides a very coarse representation of the scene structure. Second, corners are usually not available along curves, but the curves contain very rich information of the scene structure. In one example, a reconstructed scene can be represented by a Delaunay triangle mesh textured by an original reference image. The vertexes of the triangles can be matched corners and each triangle can be considered as a flat surface. Therefore, the Delaunay triangles can span across the surface boundaries, making visual appearance of the scene structure unrealistic.
Therefore, what is needed is a system and method that solves the problems that prior methods encounter with corner matching with a system that performs corner guided curve matching for scene reconstruction. What is also needed is a system the uses the relaxation framework to progressively reduce the matching.
To overcome the limitations in the prior art as described above and other limitations that will become apparent upon reading and understanding the present specification, the present invention is embodied in a curve matching system and method that is guided by a set of matched corners. The corner guided curve matching produces a geometrical representation of the scene from the images, which can be used for any suitable application, such as computer and stereo vision applications.
In general, first, multiple images depicting a scene are digitally received by the system. The images are graphical images digitally received and processed. For example, the images can be two dimensional image data, such as bitmap or raster image data. Curves of the images are then matched to correlate the two images of the scene for creating three dimensional (3D) curve information, such as 3D vector or mathematical information, of the scene. This 3D vector information can then be used in any suitable manner, for example, to digitally reconstruct the scene for stereo vision applications.
In particular, the present invention integrates corner matches into curve matching procedures. In particular, within a probabilistic framework, the role of the corner guidance is explicitly defined by a set of similarity-invariant unary measurements and by a similarity function. The similarity function provides stronger capability of resolving matching ambiguity than the epipolar constraint, and is integrated into a relaxation scheme to reduce computational complexity and improve accuracy of curve matching.
The present invention exploits the corner matching constraint and the local similarity constraint to provide stronger guidance for curve matching. The present invention operates within a probabilistic relaxation framework and the role of the corner guidance is explicitly defined by a set of similarity-invariant unary measurements and a similarity function. The unary measurements may also include conventional quantities such as the intensity in the neighborhood, orientation, and the local shape characteristics. Also, a set of uncertainty measurements for the unary measurements can be used to compute an optimal unary measurement for the complete curve. These measurements are related to the distance from a corner to a curve, and are used to control the impact of corner guidance on curve matching. The similarity function between two curves is then defined in this high dimensional measurement space.
Other aspects and advantages of the present invention as well as a more complete understanding thereof will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the invention. Moreover, it is intended that the scope of the invention be limited by the claims and not by the preceding summary or the following detailed description.